Feature Stores Archives | Page 2 of 3 | Tecton

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Managing the Flywheel of ML Data

The ML Engineer’s life has become significantly easier over the past few years, but ML projects are still too tedious and complex. Feature stores have recently emerged as an important product category within the MLOps ecosystem. They solve part of the data problem for ML by automating feature processing and serving.

But feature stores are not enough. What data teams need is a platform that automates the complete lifecycle of ML features. This platform must provide integrations with the modern DevOps and data ecosystems, including the Modern Data Stack. It should provide excellent support for advanced use cases like Recommender Systems and Fraud. And it should automate the data feedback loop, abstracting away tasks like data logging and training dataset generation. In this talk, Mike will cover his vision for the evolution of the feature store into this complete feature platform for ML. … Read More

Machine Learning Platform for Online Prediction and Continual Learning

This talk breaks down stage-by-stage requirements and challenges for online prediction and fully automated, on-demand continual learning. We’ll also discuss key design decisions a company might face when building or adopting a machine learning platform for online prediction and continual learning use cases. … Read More

Using Feast in a Ranking System

This will be a practical session explaining how Better.com uses Feast in a Ranking System that depends on multiple data sources and several models. We’ll provide a walkthrough of several architectures we considered as a team to manage features, ranking and re-ranking large volumes of entities. … Read More

Managing Data Infrastructure with Feast

Feast provides a simple framework for defining and serving machine learning features. In order to serve features reliably, with low latency and at high scale, Feast relies heavily on cloud infrastructure such as DynamoDB or AWS Lambda. This talk explains how Feast manages its serving infrastructure reliably and predictably for users. Similar to Terraform, Feast exposes commands that allow users to understand and precisely control the set of infrastructure that will be deployed to serve their features. This talk will also discuss how this process of managing data infrastructure with Feast can fit into a CICD pipeline. … Read More

ML Projects Aren’t An Island

We’ve all seen the dismal and (at this point, annoying) charts and graphs of ‘>90.x% of ML projects fail’ used as marketing ploys by various companies. What this largely simplified view of ML project success rates buries in misleading abstraction is the fact that some companies have a 100% success rate with long-running ML projects while others have a 0% success rate.

This talk is intending to go through a simple concept that is obvious to the 100% success rate companies but is a mystery to those that fail time and again. Firstly, that a project is not an island. It has dependencies on other teams (both technical and non-technical), that the DS team doesn’t need to be heroic in pursuing the most complex solution, and how establishing solid engineering practices is what will set apart the projects that will succeed and those that will fail.

The main points that will be covered:

  • Can you really solve this with ML? Should you?
  • Make sure you have the data consistently and that’s it’s not garbage (feature stores are great!)
  • Start simple and only add complexity if you need to
  • Involve the business (SMEs)
  • Build code that your team can maintain and test
  • Monitor your data and predictions so you know when things are about to break

Read More

A Tour of Features in the Wild and a Modern Solution to Manage Them

Mike will kick off the event and present his views on the different types of features commonly used for Operational ML use cases, and solutions to manage them.

Operational ML models rely on several types of features with different characteristics such as serving latencies, feature freshness, data sources, and transformation pipelines. We’ll categorize the main feature types that are most frequently encountered in real-world use cases, and discuss the specific challenges of building bespoke pipelines for each type.

We’ll present our view on a better approach to managing all common feature types. A feature platform is a system designed to manage the complete lifecycle of features. It decouples feature definitions from feature transformations, providing a higher level of abstraction that simplifies the task of building Operational ML, while using modern data infrastructure and best practice architectural patterns. … Read More

Building feature stores on Snowflake

Feature Stores cant exist without the underlying compute and storage platforms that they orchestrate. While there are many different options to choose from in the field today, there are several decisions that need to be made to ensure scalability, reliability, and performance, to a name a few.Snowflake, the Data Cloud, is a cloud agnostic data platform that offers many of the core value proposition as a traditional warehouse might, but with additional capabilities around data sharing, unstructured and semi-structured data support, and of course limitless scalability to meet the demands of your machine learning workloads. … Read More

Using Redis as your Online Feature Store: 2021 highlights & 2022 directions

With the growing business demand for real-time predictions, we are witnessing companies making investments in modernizing their data architectures to support online inference.  When companies need to deliver real-time ML applications to support large volumes of online traffic, Redis is most often selected as the foundation for the online feature store, because of its ability to deliver ultra-low latency with high throughput at scale. 2021 was a year of significant growth in customers building their online features stores with Redis. 2022 will see an increase in customers buying COTS feature store software supporting low-latency, high throughput online inference requirements.

In this talk, Redis will share key observations around customers, architectural patterns, use cases and industries adopting Redis as an online feature store. In the process, Redis will also highlight its integrations with key partners in the feature store & MLOps ecosystem including Feast, Microsoft Azure and Tecton. … Read More

New Abstractions Enabling Operational ML

High Performance Feature Serving with Feast on AWS

In this lightning talk we will showcase how teams can deploy and productionize a feature store on AWS with Feast. We will demonstrate how users can train a machine learning model using historical features from Redshift, deploy the model into production on AWS, then serve fresh feature values to the model using a serverless feature serving stack with DynamoDB. … Read More

How Shopify Contributed to Scale Feast

This talk will discuss how Shopify manages large volumes of ML data (billions of rows) using Feast. Shopify decided to adopt Feast to build their ML Feature Store in early 2021. We will speak about how we contributed to Feast to make it more scalable (example of PRs we have contributed at https://github.com/feast-dev/feast/pull/1602) … Read More

How Robinhood Built a Feature Store Using Feast

Features are essential to ML models. Therefore, a good feature infrastructure is important to any organization that wants to use ML properly in production. Feast is a great tool for building up your feature infrastructure. However, using Feast in production may need customization of your tech stack, extension for advanced use cases, and improvement of reliability and observability. In this talk, we will share the lessons learned from how Robinhood built a feature store from Feast. … Read More

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